Intelligent Communication System Research Articles

Research on channel estimation based on joint perception and deep enhancement learning in complex communication scenarios

In contemporary wireless communication systems, channel estimation and optimization have become increasingly pivotal with the growing number and complexity of devices. Communication systems frequently encounter multiple challenges, such as multipath propagation, signal fading, and interference, which may result in the degradation of communication quality, a reduction in data transmission rates, and even communication interruptions. Therefore, effective estimation and optimization of channels in complex communication environments are of paramount importance to ensure communication quality and enhance system performance. In this article, we address the intelligent, reflective surface (IRS)-assisted channel estimation problem and propose an intelligent channel estimation model based on the fusion of convolutional neural network (CNN) and gated recurrent unit (GRU) row features, utilizing the reinforcement learning Deep Deterministic Policy Gradient (DDPG) strategy for Channel Reconstruction Prediction and Generation Network (CRPG-Net). The framework initially acquires the received signal by converting the guide-frequency symbols at the transmitter into time-domain sequences to be transmitted, and after propagating through the direct channel and the IRS reflection channel, processes the data at the receiver. Subsequently, the spatial and temporal features in the received signal are extracted using the CRPG-Net model, with the adaptive optimization capability of the model enhanced by deep reinforcement learning. The introduction of reinforcement learning enables the model to continuously optimize decisions in dynamic channel environments, improve the robustness of channel estimation, and quickly adjust the IRS reflection parameters when the channel state changes to adapt to complex communication conditions. Experimental results demonstrate that the framework achieves significant channel estimation accuracy and robustness across several public datasets and real test scenarios, with the channel estimation error markedly smaller than that of traditional least squares (LS) and linear minimum mean square error (LMMSE) methods. This method introduces innovative techniques for channel estimation in intelligent communication systems, playing a crucial role in enhancing communication quality and overall system performance.

Performance Analysis of a Multi-User MIMO Reflecting Intelligent Surface-Aided Communication System Under Weibull Fading Channels.

This study analyzes the performance of a multi-user digital communication system aided by reflecting intelligent surfaces (RIS) in terms of bit error probability and secrecy outage probability for a system sending symbols with M-QAM modulation passing through channels with Weibull fading, where RIS are employed to improve the signal-to-noise plus interference ratio (SINR) for each user. The performance analysis is conducted based on the statistical properties of the phase correction error of the transmitted signal, which follows a von Mises distribution. Furthermore, this study demonstrates that the resulting SINR follows a gamma distribution, with its parameters derived analytically. The RIS performance increases the line of sight strength and reduces the secrecy outage probability and error probability when the number of reflectors is sufficiently large, even without direct links between the users and the transmitter.

Outage Probability Analysis of Relay Communication Systems for Semantic Transmission

This paper conducts an in-depth study on the outage probability performance of relay-based semantic communication systems and proposes a multi-mode intelligent relay design framework to address complex scenarios such as background knowledge differences, channel quality fluctuations, and computational limitations at the destination node. Based on a three-node two-hop communication model (source node–relay node–destination node) and integrating the DeepSC model, the study achieves cross-layer collaboration between semantic encoding/decoding and channel encoding/decoding. The proposed relay node operates in four innovative modes: semantic cooperative decode-and-forward, semantic adaptive forwarding, semantic-enhanced forwarding, and semantic-bit hybrid forwarding, each tailored to different levels of background knowledge matching, channel conditions, and computational constraints at the destination node. Through theoretical derivations, this paper presents the first closed-form expressions for the outage probability of the four relay modes, systematically quantifying the coupling effects of semantic symbol redundancy, background knowledge differences, and computational conversion efficiency on system reliability. The results show that semantic adaptive forwarding significantly reduces outage probability when background knowledge differences are minimal. When the destination node has limited computational power, the semantic-bit hybrid mode enhances communication reliability by flexibly adjusting the transmission strategy. Moreover, proper configuration of semantic symbol redundancy plays a crucial role in maintaining semantic information integrity and resisting channel interference. Monte Carlo simulations validate the theoretical analysis, demonstrating that the dynamic switching mechanism of the multi-mode relay outperforms single-mode strategies. This research provides theoretical support for reliable transmission and resource optimization in 6G semantic communication systems, uncovering the potential of joint optimization between semantic parameters and dynamic channel conditions. It holds significant implications for advancing future intelligent communication systems.

A Survey of Artificial Intelligence Enabled Channel Estimation Methods: Recent Advance, Performance, and Outlook

With the continuous advancement of wireless communication and the emergence of new communication scenarios, channel estimation, as a core component of wireless system design, has become increasingly significant. This paper reviews important advancements in channel estimation within wireless communication systems, including applications in single-input single-output (SISO), multi-input multi-output (MIMO), orthogonal time frequency space (OTFS), orthogonal frequency division multiplexing (OFDM), and the latest reconfigurable intelligent surface (RIS) systems. We first revisit traditional channel estimation methods, such as least squares (LS), minimum mean square error (MMSE), and compressed sensing (CS), and detail their fundamental principles and scopes of application. Subsequently, we discuss how deep learning techniques offer new perspectives and solutions for channel estimation through models like convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), long short-term memory (LSTM), and graph neural network (GNN), particularly in terms of their potential to handle complicated and dynamic environments. Additionally, we analyze the advantages and disadvantages of these methods in emerging scenarios, including RIS-assisted communications, vehicular networks, indoor positioning, sensing mobile networks, and satellite communications. We also address current methods for evaluating channel estimation performance and highlight the importance of standardization and open data in advancing the field. Finally, we summarize potential future directions for channel estimation and consider its prospects in sixth-generation (6 G) wireless communication systems, aiming to provide a comprehensive technical reference on channel estimation and promote the design of efficient and intelligent wireless communication systems.

Rate-Distortion-Perception Trade-Off in Information Theory, Generative Models, and Intelligent Communications.

Traditional rate-distortion (RD) theory examines the trade-off between the average length of the compressed representation of a source and the additive distortions of its reconstruction. The rate-distortion-perception (RDP) framework, which integrates the perceptual dimension into the RD paradigm, has garnered significant attention due to recent advancements in machine learning, where perceptual fidelity is assessed by the divergence between input and reconstruction distributions. In communication systems where downstream tasks involve generative modeling, high perceptual fidelity is essential, despite distortion constraints. However, while zero distortion implies perfect realism, the converse is not true, highlighting an imbalance in the significance of distortion and perceptual constraints. This article clarifies that incorporating perceptual constraints does not decrease the necessary rate; instead, under certain conditions, additional rate is required, even with the aid of common and private randomness, which are key elements in generative models. Consequently, we project an increase in expected traffic in intelligent communication networks with the consideration of perceptual quality. Nevertheless, a modest increase in rate can enable generative models to significantly enhance the perceptual quality of reconstructions. By exploring the synergies between generative modeling and communication through the lens of information-theoretic results, this article demonstrates the benefits of intelligent communication systems and advocates for the application of the RDP framework in advancing compression and semantic communication research.

Cognitive intelligence routing protocol for disaster management and underwater communication system in underwater acoustic network

Future communication paradigms, such as 6G networks, emphasize self-sustainability, intelligent networking, and secure, adaptive communication. This research presents an innovative routing framework tailored for Underwater Sensor Networks (UWSNs) and Underwater Acoustic Networks (UANs), addressing critical challenges like energy constraints, security vulnerabilities, limited bandwidth, and interference. The proposed system integrates a Multi-Agent System (MAS), blockchain technology, and acoustic communication to enhance security, optimize energy usage, and improve data transmission efficiency. Key features include intelligent node mechanisms, proactive bandwidth and interference management, a multi-hop paradigm, distance-aware longevity strategies, and robust cryptographic protocols. The system is benchmarked against established routing protocols such as GCORP, PER, MARL-MC, and MLAR, demonstrating superior performance. The proposed cognitive intelligence (CI) protocol achieves energy consumption below 120 J per transmission, significantly lower than existing methods. It also achieves end-to-end latency under two seconds in multi-hop scenarios, outperforming alternatives like MARL-MC and GCORP. Additionally, the CI protocol exhibits a packet delivery ratio (PDR) exceeding 90% and an extended network lifetime surpassing 1850 s, making it a robust solution for resource-constrained underwater environments. This work not only addresses the unique demands of underwater networks but also contributes to the vision of self-sustainable and intelligent communication systems, aligning with the broader context of 6G paradigms through energy-efficient routing, cognitive intelligence, and secure, adaptive communication frameworks. The results underscore the effectiveness of the CI protocol in enhancing energy efficiency, reducing latency, and ensuring reliable long-term operation, thereby supporting critical applications like disaster management and environmental monitoring.

Deep Reinforcement Learning-Based Secrecy Rate Optimization for Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surface-Assisted Unmanned Aerial Vehicle-Integrated Sensing and Communication Systems.

This study investigates security issues in a scenario involving a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted unmanned aerial vehicle (UAV) with integrated sensing and communication (ISAC) functionality (UAV-ISAC). In this scenario, both legitimate users and eavesdropping users are present, which makes security a crucial concern. Our research goal is to extend the system's coverage and improve its flexibility through the introduction of STAR-RIS, while ensuring secure transmission rates. To achieve this, we propose a secure transmission scheme through jointly optimizing the UAV-ISAC trajectory, transmit beamforming, and the phase and amplitude adjustments of the STAR-RIS reflective elements. The approach seeks to maximize the average secrecy rate while satisfying communication and sensing performance standards and transmission security constraints. As the considered problem involves coupled variables and is non-convex, it is difficult to solve using traditional optimization methods. To address this issue, we adopt a multi-agent deep reinforcement learning (MADRL) approach, which allows agents to interact with the environment to learn optimal strategies, effectively dealing with complex environments. The simulation results demonstrate that the proposed scheme significantly enhances the system's average secrecy rate while satisfying communication, sensing, and security constraints.

Few-Shot Learning with Multimodal Fusion for Efficient Cloud–Edge Collaborative Communication

As demand for high-capacity, low-latency communication rises, mmWave systems are essential for enabling ultra-high-speed transmission in fifth-generation mobile communication technology (5G) and upcoming 6G networks, especially in dynamic, data-scarce environments. However, deploying mmWave systems in dynamic environments presents significant challenges, especially in beam selection, where limited training data and environmental variability hinder optimal performance. In such scenarios, computation offloading has emerged as a key enabler, allowing computationally intensive tasks to be shifted from resource-constrained edge devices to powerful cloud servers, thereby reducing latency and optimizing resource utilization. This paper introduces a novel cloud–edge collaborative approach integrating few-shot learning (FSL) with multimodal fusion to address these challenges. By leveraging data from diverse modalities—such as red-green-blue (RGB) images, radar signals, and light detection and ranging (LiDAR)—within a cloud–edge architecture, the proposed framework effectively captures spatiotemporal features, enabling efficient and accurate beam selection with minimal data requirements. The cloud server is tasked with computationally intensive training, while the edge node focuses on real-time inference, ensuring low-latency decision making. Experimental evaluations confirm the model’s robustness, achieving high beam selection accuracy under one-shot and five-shot conditions while reducing computational overhead. This study highlights the potential of combining cloud–edge collaboration with FSL and multimodal fusion for next-generation wireless networks, paving the way for scalable, intelligent, and adaptive mmWave communication systems.

A compensation of VANETs with UAVs to optimizing the routing strategy

VANETs (Vehicular Ad Hoc Networks) played a vital role in the infrastructure of smart cities due to intelligent communication systems. VANETs exhibit unique characteristics, such as high speeds and unpredictable mobility, which lead to challenges such as overhead, delay, and link failure. Therefore, Unmanned Aerial Vehicles (UAVs) are introduced to mitigate ground-level issues, with communication managed through the air medium used to address these problems. Routing also needs to be enhanced for effective communication in UAV-assisted VANETs. This paper proposes an Advanced UAV Communication Strategy for VANETs (AUCS-VANETs) approach to optimize the routing protocol. The strategy selects the shortest route from source to calculate the vehicle's potential future delay and employs the Probability Distribution Function (PDF) through Bayes' model. We use NS2 and SUMO in the simulation as well as four parameters for testing (“Packet Delivery Ratio” (PDR), “End-To-End” (E2E) delay, “Routing Overhead” (RO), and “Throughput” (TP). The experiment results show that the AUCS-VANETs approach achieves higher PDR and TP, along with lower E2E delay and RO compared to earlier approaches.

Intelligent Optical Communication System: Harnessing Reinforcement Learning Techniques for Improved Performance and Optimization

The increasing demand for high-speed and reliable data transmission has necessitated the development of Intelligent Optical Communication Systems. This study proposes optimizing optical communication systems using Reinforcement Learning (RL) techniques to enhance optical network performance. The Intelligent Optical Communication System leverages the RL algorithm to adaptively adjust system parameters to address network challenges resulting from bandwidth allocation, power management, adaptive modulation, routing inefficiency, inequalization, nonlinear impairment models, and Bit Error Rate (BER), to predictively maintain and enhance network performance and error minimization. We adopted a comprehensive simulation framework that includes quantitative assessments of various parameters. Results show that bandwidth allocation varied from 100 to 150 Mbps, while power allocation ranged between 10 to 30 dBm. Adaptive modulation strategies effectively utilized formats like QPSK, 16-QAM, and 64-QAM, resulting in dynamic modulation selection over time. The study recorded average routing delays up to 50ms and showcased the received signal's amplitude using equalization techniques. Nonlinear impairment modeling revealed a quadratic relationship between signal power and nonlinear impairment levels, indicating that higher signal powers could significantly degrade signal quality. The BER was approximately 2%, reflecting the system’s effectiveness in maintaining data integrity. Predictive maintenance analysis demonstrated varying failure probabilities and maintenance impacts, emphasizing the need for ongoing monitoring to reduce service interruptions. Conclusively, the overall results indicate that integrating RL into optical communication systems can significantly enhance operational efficiency and adaptability. Ultimately, these align with policy goals that focus on improving reliability and service quality in telecommunications. This work contributes to advancing optical communication technology, paving the way for more efficient and reliable data transmission systems in an increasingly data-driven world.

Recent Advances in Automatic Modulation Classification Technology: Methods, Results, and Prospects

As an essential technology for spectrum sensing and dynamic spectrum access, automatic modulation classification (AMC) is a critical step in intelligent wireless communication systems, aiming at automatically recognizing the modulation schemes of received signals. In practice, AMC is challenging due to the influence of communication environment and signal parameters, such as unknown channels, noise, symbol rate, signal length, and sampling frequency. In this survey, we investigated a series of typical AMC methods, including key technology, performance comparisons, advantages, challenges, and future key development directions. According to the methodology and processing flow, AMC methods are divided into three categories: likelihood‐based (Lb) methods, feature‐based (Fb) methods, and deep learning methods. The technical details of various types of methods are introduced and discussed, such as likelihood distributions, artificial features, classifiers, and network structures. Then, extensive experimental results of state‐of‐the‐art AMC methods on public or simulated datasets are compared and analyzed. Despite the achievements that have been made, there are still limitations of the individual methods, including generalization capability, reasoning efficiency, model complexity, and robustness. In the end, we summarized the severe challenges faced by AMC and key future research directions.

An Intelligent Q-Learning-Based Routing Model

The rising speed of data transmission requires modern technology to meet its essential requirement of network communication efficiency through effective routing techniques. Routers act as the central elements of this functionality which demonstrates why their optimal performance needs attention. This work introduces an intelligent routing design that applies machine learning approaches for network performance optimization under dynamic network environments. The fluctuating environments decrease the effectiveness of traditional routing protocols including RIP, BGP and OSPF while causing their routing performance to deteriorate. The proposed model implements machine learning-based decision adjustment methods that apply current network information to dynamically reroute data. The routing system uses supervised and unsupervised learning approaches to predict network traffic congestions and choose the most suitable routes. Network performance optimization relies on the incorporation of latency, bandwidth, packet loss, congestion, jitter, reliability, energy efficiency in addition to cost parameters before training occurs using historical network information. Python-based development achieved enhanced network throughput in addition to faster operation with better adaptability and resource-efficient management of changing network conditions. Machine learning arrives as a transformative force for network routing through adaptive intelligent communication systems which surpass traditional protocols for modern networking requirements.

AEVBComm: an intelligent communication system based on $$\beta$$-VAE

AEVBComm: an intelligent communication system based on $$\beta$$-VAE

GAO–FCNN–Enabled Beamforming of the RIS–Assisted Intelligent Communication System

The joint beamforming optimization from the perspective of the bit error rate (BER) in a reconfigurable intelligent surface (RIS)–assisted intelligent communication system is studied in this paper. A genetic algorithm (GA) is investigated to address the bottleneck of the system performance based on the dynamic adaptability theory. However, the bottleneck is caused by the interaction between the active and passive beamforming. To tackle the constraints of conventional optimization approaches, the hybrid scheme is proposed to combine the GA optimization (GAO) and fully connected neural network (FCNN) strategy. Specifically, the intelligent collaborative tuning of system parameters is achieved using this proposed technique. Simulation findings indicate that the hybrid scheme not only simplifies the calculation process to obtain the optimal network parameters, but also effectively optimizes the system structure by dynamically adjusting the RIS reflection configuration. Based on this, the signal transmission quality is improved, interference is reduced, and the stable and efficient operation of the RIS–assisted intelligent communication system is ensured in the complex wireless transmission scenario.

Rate Optimization of Intelligent Reflecting Surface-Assisted Coal Mine Wireless Communication Systems.

This paper proposes a three-step joint rate optimization method for intelligent reflecting surface (IRS)-assisted coal mine wireless communication systems. Different from terrestrial IRS-assisted communication scenarios, in coal mines, IRSs can be installed flexibly on the tops of rectangular tunnels to address the issues of signals being blocked and interfered with by mining equipment. Therefore, it is necessary to optimize the IRS deployment position, the transmit power and IRS phase shifts to achieve the maximum effective achievable rate at user stations equipped with the proposed system. However, due to the complex channel models of coal mines, the optimization problem of IRS deployment position is non-convex. To solve this problem, two auxiliary variables along with logarithmic operations and Taylor approximation are introduced. On this basis, a three-step joint rate optimization involving the transmit power, IRS phase shifts and IRS deployment position is proposed to maximize the effective achievable rates at the user station. The simulation results show that compared with other rate optimization schemes, the effective achievable rates at the user station using the proposed joint rate optimization scheme can be improved by approximately 12.32% to 54.17% for different parameter configurations. It is also pointed out that the deployment position of the IRS can converge to the same optimal position independent of the initial deployment position. Moreover, we investigate the effects of the roughness of the tunnel walls in a coal mine on the effective achievable rates at the user station, and the simulation results indicate that the proposed three-step joint rate optimization scheme performs better in the coal mine scenario regardless of the roughness.

A certificateless designated verifier sanitizable signature in e-health intelligent mobile communication system

A certificateless designated verifier sanitizable signature in e-health intelligent mobile communication system

Research on the Design and Optimization of Intelligent Electronic Communication System Based on the Internet of Things Technology

With the development and progress of science and technology, the Internet of Things technology has become the key technology for the innovation and development of various industries, especially in the electronic communication industry has played an important role. This study designed and optimized the intelligent electronic communication system based on the Internet of Things technology. Firstly, through the deep understanding and analysis of Internet of Things technology and electronic communication system, the feasibility and necessity of applying Internet of Things technology to electronic communication system are determined. Secondly, according to the needs of the electronic communication system, intelligent design, including hardware selection, software programming, and network protocol customization. Then, the optimization strategy of the electronic communication system based on the Internet of Things is proposed, which mainly involves the key fields such as energy saving, security and data processing speed improvement. The experiment proves that the optimized electronic communication system not only has higher transmission efficiency, but also can effectively reduce energy consumption and enhance the security of the system, thus greatly improving the effect and performance of electronic communication. This study enriches the application of Internet of Things technology in the field of electronic communication, but also provides useful reference and enlightenment for research in other related fields.

Accident Prevention System

Accident prevention system represents a groundbreaking innovation in vehicular safety technology. This advanced system integrates cutting- edge technologies such as eye blink sensors, GPS modules, Arduino and GSM communication to detect and mitigate potential hazards during driving. The accident prevention system incorporates a GPS module, enabling precise location tracking and providing crucial data for emergency responders. The integration of these technologies aims to significantly reduce the risk of accidents caused by driver inattention, fatigue, or other safety-critical factors. This abstract outline the comprehensive approach of accident prevention system, highlighting its potential to revolutionize road safety by leveraging state-of-the-art sensor technologies and intelligent communication systems. Key Words: Eye blink sensor, Arduino, GPS Module, GSM Module.

Root Check: Real-time Plant Health Monitoring and Irrigation Control

Ensuring the stability and profitability of aquatic plants is a critical concern in modern agriculture. This research seeks to develop an intelligent communication system for monitoring plant health. Despite the advanced data visualization and forecasting capabilities of existing technologies, many farmers are not fully utilizing them. Our innovative technology analyzes key factors like temperature and humidity to make informed decisions about water pumping from the generator. System reduces the complexity of managing plant health, converting sensor data into meaningful visuals in platforms like Adafruit Cloud and IoT interfaces. Additionally, the system sends emails to notify farmers about the real time status of the farm. (Saving water and avoiding regular maintenance). Combining ancient farming methods with modern innovations is the goal of the Trans Agriculture technique.

ANALYSIS OF THE IMPACT OF SHARDING ON THE SCALABILITY AND EFFICIENCY OF BLOCKCHAIN TECHNOLOGIES FOR THE CREATION OF INFORMATION-ANALYTICAL SYSTEMS FOR ENVIRONMENTAL MONITORING OF EMISSIONS INTO THE ENVIRONMENT

This study examines the impact of sharding on the scalability and efficiency of blockchain systems, specifically in the development of a complex of intelligent information and communication systems for environmental monitoring of emissions into the environment for decision-making in the context of carbon neutrality. Utilizing the Ikarus Network infrastructure, sharding was implemented on masternodes as a key technology to optimize transaction processing. Sharding enables the blockchain to be divided into multiple parallel chains, significantly increasing throughput and reducing the load on individual nodes. The results demonstrate a 70% increase in transaction processing speed, allowing the system to handle up to 5000 transactions per second, compared to the previous 3000 transactions per second. Network throughput increased by 50%, ensuring more efficient load distribution and stable operation even with high data volumes. Statistical analysis using ANOVA confirmed significant improvements in transaction processing speed, confirmation time, and resource usage post-sharding implementation. The F-value for transaction processing speed was 4567 with a P-value of 0.0001, indicating substantial improvements. Visual data analysis further confirmed these results, showing noticeable performance enhancements in the blockchain system. Distribution charts and histograms of transaction processing speed and confirmation time revealed an increase in the average number of transactions per second and greater system stability post-sharding. Sharding not only increased throughput but also enhanced system security by decentralizing data among shards, complicating potential cyberattacks. The study aimed to determine how sharding can improve the scalability and efficiency of blockchain systems. These improvements position the Ikarus Network as a promising solution for scalable and secure blockchain-based applications, especially for tasks related to carbon emission monitoring and management. These findings can underpin further study and the development of more efficient blockchain technologies.